
Cultivated grasslands are of great importance to national strategies and in synergy with Common Agricultural Policy(CAP) there is an emerging demand for new innovative methods capable of monitoring vegetation in large areas over growing periods. Vegetation of this kind produces a large volume of biomass that is economically valuable for various uses. Deep learning methods have shown their potential in combination with Earth Observation data, but data availability for mowing detection task remains low. To combat the lack of available data, we developed this dataset for three different Regions of Interest in Greece that contains over 1600 different parcels. This dataset is part of the paper 'Large Scale Mowing Event Detection on Dense Time Series Data Using Deep Learning Methods and Knowledge Distillation' published in ISPRS Archives (EARSEL 44th Symposium 2025).
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
